/*
 *  PerlinNoise.cpp
 *  Fracture
 *
 *  Created by Jamie Portsmouth on 20/02/2010.
 *  Copyright 2010 __MyCompanyName__. All rights reserved.
 *
 */

#include "PerlinNoise.h"


/* Coherent noise function over 1, 2 or 3 dimensions */
/* (copyright Ken Perlin) */

#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include "PerlinNoise.h"

#define B 0x100
#define BM 0xff
#define Nperlin 0x1000
#define NM 0xfff

#define s_curve(t) ( t * t * (3. - 2. * t) )
#define lerp(t, a, b) ( a + t * (b - a) )
#define setup(i,b0,b1,r0,r1)\
t = vec[i] + Nperlin;\
b0 = ((int)t) & BM;\
b1 = (b0+1) & BM;\
r0 = t - (int)t;\
r1 = r0 - 1.;
#define at2(rx,ry) ( rx * q[0] + ry * q[1] )
#define at3(rx,ry,rz) ( rx * q[0] + ry * q[1] + rz * q[2] )

static int p[B + B + 2];
static double g3[B + B + 2][3];
static double g2[B + B + 2][2];
static double g1[B + B + 2];
static int start = 1;

namespace PerlinNoise
{
	

double noise1(double arg)
{
	int bx0, bx1;
	double rx0, rx1, sx, t, u, v, vec[1];
	
	vec[0] = arg;
	if (start) {
		start = 0;
		init();
	}
	
	setup(0,bx0,bx1,rx0,rx1);
	
	sx = s_curve(rx0);
	u = rx0 * g1[ p[ bx0 ] ];
	v = rx1 * g1[ p[ bx1 ] ];
	
	return(lerp(sx, u, v));
}

double noise2(double vec[2])
{
	int bx0, bx1, by0, by1, b00, b10, b01, b11;
	double rx0, rx1, ry0, ry1, *q, sx, sy, a, b, t, u, v;
	int i, j;
	
	if (start) {
		start = 0;
		init();
	}
	
	setup(0, bx0,bx1, rx0,rx1);
	setup(1, by0,by1, ry0,ry1);
	
	i = p[ bx0 ];
	j = p[ bx1 ];
	
	b00 = p[ i + by0 ];
	b10 = p[ j + by0 ];
	b01 = p[ i + by1 ];
	b11 = p[ j + by1 ];
	
	sx = s_curve(rx0);
	sy = s_curve(ry0);
	
	q = g2[ b00 ] ; u = at2(rx0,ry0);
	q = g2[ b10 ] ; v = at2(rx1,ry0);
	a = lerp(sx, u, v);
	
	q = g2[ b01 ] ; u = at2(rx0,ry1);
	q = g2[ b11 ] ; v = at2(rx1,ry1);
	b = lerp(sx, u, v);
	
	return lerp(sy, a, b);
}

double noise3(double vec[3])
{
	int bx0, bx1, by0, by1, bz0, bz1, b00, b10, b01, b11;
	double rx0, rx1, ry0, ry1, rz0, rz1, *q, sy, sz, a, b, c, d, t, u, v;
	int i, j;
	
	if (start) {
		start = 0;
		init();
	}
	
	setup(0, bx0,bx1, rx0,rx1);
	setup(1, by0,by1, ry0,ry1);
	setup(2, bz0,bz1, rz0,rz1);
	
	i = p[ bx0 ];
	j = p[ bx1 ];
	
	b00 = p[ i + by0 ];
	b10 = p[ j + by0 ];
	b01 = p[ i + by1 ];
	b11 = p[ j + by1 ];
	
	t  = s_curve(rx0);
	sy = s_curve(ry0);
	sz = s_curve(rz0);
	
	q = g3[ b00 + bz0 ] ; u = at3(rx0,ry0,rz0);
	q = g3[ b10 + bz0 ] ; v = at3(rx1,ry0,rz0);
	a = lerp(t, u, v);
	
	q = g3[ b01 + bz0 ] ; u = at3(rx0,ry1,rz0);
	q = g3[ b11 + bz0 ] ; v = at3(rx1,ry1,rz0);
	b = lerp(t, u, v);
	
	c = lerp(sy, a, b);
	
	q = g3[ b00 + bz1 ] ; u = at3(rx0,ry0,rz1);
	q = g3[ b10 + bz1 ] ; v = at3(rx1,ry0,rz1);
	a = lerp(t, u, v);
	
	q = g3[ b01 + bz1 ] ; u = at3(rx0,ry1,rz1);
	q = g3[ b11 + bz1 ] ; v = at3(rx1,ry1,rz1);
	b = lerp(t, u, v);
	
	d = lerp(sy, a, b);
	
	return lerp(sz, c, d);
}

void normalize2(double v[2])
{
	double s;
	
	s = sqrt(v[0] * v[0] + v[1] * v[1]);
	v[0] = v[0] / s;
	v[1] = v[1] / s;
}

void normalize3(double v[3])
{
	double s;
	
	s = sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
	v[0] = v[0] / s;
	v[1] = v[1] / s;
	v[2] = v[2] / s;
}

void init(void)
{
	int i, j, k;
	
	for (i = 0 ; i < B ; i++) {
		p[i] = i;
		g1[i] = (double)((random() % (B + B)) - B) / B;
		
		for (j = 0 ; j < 2 ; j++)
			g2[i][j] = (double)((random() % (B + B)) - B) / B;
		normalize2(g2[i]);
		
		for (j = 0 ; j < 3 ; j++)
			g3[i][j] = (double)((random() % (B + B)) - B) / B;
		normalize3(g3[i]);
	}
	
	while (--i) {
		k = p[i];
		p[i] = p[j = random() % B];
		p[j] = k;
	}
	
	for (i = 0 ; i < B + 2 ; i++) {
		p[B + i] = p[i];
		g1[B + i] = g1[i];
		for (j = 0 ; j < 2 ; j++)
			g2[B + i][j] = g2[i][j];
		for (j = 0 ; j < 3 ; j++)
			g3[B + i][j] = g3[i][j];
	}
}

/* --- My harmonic summing functions - PDB --------------------------*/

/*
 In what follows "alpha" is the weight when the sum is formed.
 Typically it is 2, As this approaches 1 the function is noisier.
 "beta" is the harmonic scaling/spacing, typically 2.
 */

double PerlinNoise1D(double x,double alpha,double beta,int n)
{
	int i;
	double val,sum = 0;
	double p,scale = 1;
	
	p = x;
	for (i=0;i<n;i++) {
		val = noise1(p);
		sum += val / scale;
		scale *= alpha;
		p *= beta;
	}
	return(sum);
}

double PerlinNoise2D(double x,double y,double alpha,double beta,int n)
{
	int i;
	double val,sum = 0;
	double p[2],scale = 1;
	
	p[0] = x;
	p[1] = y;
	for (i=0;i<n;i++) {
		val = noise2(p);
		sum += val / scale;
		scale *= alpha;
		p[0] *= beta;
		p[1] *= beta;
	}
	return(sum);
}

double PerlinNoise3D(double x,double y,double z,double alpha,double beta,int n)
{
	int i;
	double val,sum = 0;
	double p[3],scale = 1;
	
	p[0] = x;
	p[1] = y;
	p[2] = z;
	for (i=0;i<n;i++) {
		val = noise3(p);
		sum += val / scale;
		scale *= alpha;
		p[0] *= beta;
		p[1] *= beta;
		p[2] *= beta;
	}
	return(sum);
}
	
}
